CMSC 341 Binary Heaps Priority Queues

2 Priority: some property of an object that allows it to be prioritized WRT other objects (of the same type) Priority Queue: homogeneous collection of Comparables with the following operations (duplicates are allowed) –void insert (const Comparable &x) –void deleteMin( ) –void deleteMin ( Comparable & min) –const Comparable &findMin( ) const –Construct from a set of initial values –bool isEmpty( ) const –bool isFull( ) const –void makeEmpty( )

3 Priority Queue Applications Printer management: the shorter document on the printer queue, the higher its priority. Jobs queue: users’ tasks are given priorities. System priority high. Simulations Sorting

4 Possible Implementations Use a sorted list. Sort by priority upon insertion. –findMin( ) --> Itr.retrieve( ) –insert( ) --> list.insert( ) –deleteMin( )--> list.delete( 1 ) Use ordinary BST –findMin( ) --> tree.findMin( ) –insert( ) --> tree.insert( ) –deleteMin( )--> tree.delete( tree.findMin( ) ) Use balanced BST –guaranteed O(lg n) for Red-Black

5 Binary Heap A binary heap is a Complete Binary Tree (CBT) with the further property that at every node neither child is smaller than (or equivalently, both children are at least as large as) the value in that node. This property is called a partial ordering. As a result of this partial ordering, every path from the root to a leaf visits nodes in a non-decreasing order.

6 Binary Heap Performance Performance –constructionO( n ) –findMinO( 1 ) –insertO( lg n ) –deleteMinO( lg n ) Heap efficiency results, in part, from the implementation –conceptually a complete binary tree –implementation in an array/vector (in level order) with the root at index 1

7 Binary Heap Properties For a node at index i –its left child is at index 2i –its right child is at index 2i+1 –its parent is at index  i/2  No pointer storage Fast computation of 2i and  i/2  i << 1 = 2i i >> 1 =  i/2 

8 BinaryHeap.H template class Binary Heap { public: explicit BinaryHeap(int capacity = BIG); bool isEmpty() const; bool isFull() const; const Comparable & findMin() const; void insert (const Comparable & x); void deleteMin(); void deleteMin(Comparable & min_item); void makeEmpty(); private: int currentSize; vector array; void buildHeap(); void percolateDown(int hole); };

9 BinaryHeap.C template const Comparable & BinaryHeap::findMin( ) { if ( isEmpty( ) ) throw Underflow( ); return array[1]; }

10 Insert Operation Must maintain –CBT property (heap shape): easy, just insert new element at “the right” of the array –Heap order could be wrong after insertion if new element is smaller than its ancestors continuously swap the new element with its parent until parent is not greater than it –called sift up or percolate up Performance of insert is O( lg n ) in the worst case because the height of a CBT is O( lg n )

11 BinaryHeap.C (cont) template void BinaryHeap :: insert(const Comparable & x) { if (isFull()) throw OverFlow(); int hole = ++currentSize; // percolate up for (; hole > 1 && x < array[hole/2]; hole /= 2) array[hole] = array[hole/2]; // put x in hole array[hole] = x; }

12 Deletion Operation Steps –remove min element (the root) –maintain heap shape – maintain heap order To maintain heap shape, actual node removed is “last one” in the array –replace root value with value from last node and delete last node –sift-down the new root value continually exchange value with the smaller child until no child is smaller

13 BinaryHeap.C (cont) template void BinaryHeap :: deleteMin(Comparable & minItem) { if ( isEmpty( ) ) throw Underflow( ); minItem = array[1]; array[1] = array[currentSize--]; percolateDown(1); }

14 BinaryHeap.C (cont) template void BinaryHeap ::percolateDown(int hole) { int child; Comparable tmp = array[hole]; for (; hole * 2 <= currentSize; hole = child) { child= hole * 2; if (child != currentSize && array[child + 1] < array[ child ] ) child++; if (array [child] < tmp) array[ hole ] = array[ child ]; else break; } array[hole] = tmp; }

15 Constructing a Binary Heap A BH can be constructed in O(n) time. Suppose we are given an array of objects in an arbitrary order. Since it’s an array with no holes, it’s already a CBT. It can be put into heap order in O(n) time. –Create the array and store n elements in it in arbitrary order. O(n) to copy all the objects. –Heapify the array start at ndode i =  n/2  –percolateDown(i) repeat for all nodes from i down to 1 (the root)

16 BinaryHeap.C (cont) template void BinaryHeap :: buildHeap( ) { for(int i = currentSize/2; i >0; i--) percolateDown(i); }

17 Performance of Construction A CBT has 2 h-1 nodes on level h-1. On level h-l, at most 1 swap is needed per node. On level h-2, at most 2 swaps are needed. … On level 0, at most h swaps are needed. Number of swaps = S = 2 h *0 + 2 h-1 *1 + 2 h-2 *2 + … *h = = h(2 h+1 -1) - ((h-1)2 h+1 +2) = 2 h+1 (h-(h-1))-h-2 = 2 h+1 -h-2

18 Performance of Construction (cont) But 2 h+1 -h-2= O(2 h ) But n = … + 2 h = Therefore, n = O(2 h ) So S = O(n) A heap of n nodes can be built in O(n) time.

19 Heap Sort Given n values, can sort in O(n log n) time (in place). –Insert values into array -- O(n) –heapify -- O(n) –repeatedly delete min -- O(lg n), n times Using a min heap, this code sorts in reverse order. With a max heap, it sorts in normal order. for (i = n-1; i >= 1; i--) { x =findMin(); deleteMin(); A[i+1] = x; }

20 Limitations Binary heaps support insert, findMin, deleteMin, and construct efficiently. They do not efficiently support the meld or merge operation in which 2 PQs are merged into one. If P 1 and P 2 are of size n 1 and n 2, then the merge is in O(n 1 + n 2 )

21 Leftist Heap Supports –findMin-- O(1) –deleteMin-- O(lg n) –insert-- O(lg n) –construct-- O(n) –merge-- O(lg n)

22 Leftist Tree The null path length (npl) of a node, X, is the length of the shortest path from X to a node without two children (a non-full node). An LT is a binary tree in which at each node X, the null path length of X’s right child is not larger than the null path length of the X’s left child. I.e The length of the path from X’s right child to its nearest non-full node is not larger than the length of the path from X’s left child to its nearest non-full node. An important property of leftist trees: –At every node, the shortest path to a non-full node is along the rightmost path. –Suppose this was not true. Then, at the same node the path on the left would be shorter than the path on the right.

23 Leftist Heap A leftist heap is a leftist tree in which the values in the nodes obey heap order (the tree is partially ordered). Since a LH is not necessarily a CBT we do not implement it in an array. An explicit tree implementation is used. Operations –findMin-- return root value, same as BH –deleteMin -- done using meld operation –insert-- done using meld operation –construct -- done using meld operation

24 Meld Algorithm: Meld (H1, H2) { if (!root(H1) || (root_value(H1) > root_value(H2) ) swap (H1, H2) if (root(H1) != NULL)) right(H1) <-- Meld(right(H1),H2) if (left_length(H1) < right_length(H1) swap(left(H1), right(H1); }

25 Meld (cont) Performance:O( lg n ) –the rightmost path of each tree has at most  lg(n+1)  nodes. So O( lg n ) nodes will be involved.

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29 Leftist Heap Operations Other operations implemented in terms of Meld –insert (item) make item into a 1-node LH, X Meld(*this, X) –deleteMin Meld(left subtree, right subtree) –construct from N items make N LHs from the N values, one element in each meld each in –one at a time : –use queue and build pairwise :

30 LH Construct Algorithm: –make N leftist heaps, H 1 ….H N each with one data value –Instantiate Queue Q; –for (I = 1; I <= N; I++) Q.Enqueue(H i ); –Heap H = Q.Dequeue( ); –while (!Q.IsEmpty( ) ) Q.Enqueue( meld( H, Q.Dequeue( ) ) ); H = Q.Dequeue( );